Amazon SageMaker Algorithm for Content Moderation

Amazon Comprehend

Question

You work as a machine learning specialist for an online retail company that sells health products.

Your company allows users to enter reviews of the products they buy from the website.

You want to make sure the reviews do not contain any offensive or unsafe content, such as obscenities or threatening language. Which Amazon SageMaker algorithm or Amazon service will allow you to scan your user's review text in the simplest way?

Answers

Explanations

Click on the arrows to vote for the correct answer

A. B. C. D.

Answer: D.

Option A is incorrect.

The BlazingText algorithm is used for natural language processing tasks like sentiment analysis, and named entity recognition.

You should use all of these features when scanning your user's review text.

However, the BlazingText algorithm requires more developer effort and time than using the Comprehend service.

Option B is incorrect.

The Neural Topic Model algorithm is used to group documents into topics using the statistical distribution of words within the documents.

This algorithm would not be the most efficient choice for detecting offensive or unsafe language.

Option C is incorrect.

The Semantic Segmentation algorithm is used for computer vision applications.

So it is not an algorithm you would use for text analysis.

Option D is correct.

The Comprehend service scans your unstructured review text and analyzes it using SageMaker Natural Language Processing (NLP) algorithms to find key phrases, entities, and sentiments.

This is the most expeditious and efficient option.

Reference:

Please see the Amazon SageMaker developer guide titled Using Amazon SageMaker Built-in Algorithms, and the Amazon Machine Learning blog titled Analyze content with Amazon Comprehend and Amazon SageMaker notebooks.

Here is a diagram of the solution:

Reviews

Input

Entities

Key Phrases

Language

Sentiment

Topics

Output

The most suitable Amazon SageMaker algorithm or Amazon service to scan user's reviews for offensive or unsafe content is Amazon Comprehend (option D).

Amazon Comprehend is a natural language processing service that uses machine learning to find insights and relationships in text data. It can detect the sentiment of the text, identify key phrases, and extract relevant entities from the text. Additionally, Comprehend can also detect offensive or unsafe content such as obscenities or threatening language.

By using Comprehend, the online retail company can analyze the reviews submitted by users and flag any inappropriate content. Comprehend can also provide a confidence score for the detected offensive language or unsafe content, which can help the company prioritize which reviews to review first.

BlazingText (option A) is a SageMaker algorithm for text classification and word embeddings. While it is possible to use BlazingText to detect offensive language, it would require the company to train their own custom model and may not be as accurate as using Comprehend.

Neural Topic Model (option B) is another SageMaker algorithm that can be used for topic modeling, but it is not designed for offensive language detection.

Semantic Segmentation (option C) is a computer vision technique for image segmentation, and is not applicable to text analysis.

In summary, the most suitable option for scanning user's reviews for offensive or unsafe content is Amazon Comprehend.